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Aplicaciones anidadas

Aplicaciones anidadas


The Institute of Data Science and Artificial Intelligence (DATAI) announces the DATAI Awards 2023-2024 for particularly relevant scientific contributions in the field of Data Science and Artificial Intelligence. The prizes recognize originality, innovation and contribution with the double objective of encouraging research work in this field as well as its projection to society.




A maximum of two prizes will be awarded in this call, each with an endowment of 3,000 euros as aid for the development of a research project. The award-winning contributions must be pioneering and influential in international research. If the prize is awarded to an application submitted by two or more members of the institute, one of them will act as the person responsible for the aid.

The awards are aimed at DATAI member researchers, in any of its modalities, full, associated or invited members. The contribution will mention DATAI among the authors' affiliations, not being enough a mention in the acknowledgments. This is the way to do it:

Institute of Data Science and Artificial Intelligence (DATAI), Universidad de Navarra, Edificio Ismael Sánchez Bella, Campus Universitario, 31009-Pamplona, Spain

All the necessary documentation related to the awards will be sent in digital format to from November 2, 2023 until August 31, 2024.

The awards will be announced by October 30, 2024.



Aplicaciones anidadas


The scientific committee of the Institute of Data Science and Artificial Intelligence (DATAI) has made a decision for the evaluation of the 2022-2023 DATAI Awards.


  • Amparo Alonso Betanzos - Universidade da Coruña

  • Enrique del Castillo - The Pennsylvania State University

  • John Stufken - George Mason University

The committee wants to stress all the papers in the competition were very interesting. It was not easy to evaluate the papers since they came from different areas.




Applied contribution


Contribution: Compendium of 4 contributions:

1. Persistence in UK Historical Data on Life Expectancy.
2. Long Memory Cointegration in the Analysis of Maximum, Minimum and Range Temperatures in Africa: Implications for Climate Change.
3. Measuring Persistence in the US Equity Gender Diversity Index.
4. Energy prices in Europe. Evidence of persistence across market.

Author(s): Guglielmo Maria Caporale, Juan Infante, Marta del Rio, Olaoluwa S. Yaya, Oluwaseun A. Adesina, Hammed A. Olayinka, Oluseyi E. Ogunsola, Miguel A. Martin-Valmayor, Luis A. Gil-Alana

Brief description of the qualities motivating the award: This is a compilation of four articles that address fractional integration and cointegration along with their empirical implementations. The first of these articles examines the historical evolution of life expectancy in the United Kingdom. The second article focuses on climate change in Africa and once again uses techniques of fractional integration and cointegration. The third article investigates gender diversity equity in the United States. Finally, the fourth article centers on the study of energy prices in Europe.



Methodological contribution


Contribution: Precision oncology: a review to assess interpretability in several explainable methods.

Author(s): Marian Gimeno, Katyna Sada del Real, Angel Rubio.

Brief description of the qualities motivating the award: In this paper, a novel algorithm called "Optimal Decision Trees" was introduced, whose goal is precisely to solve the PM problem. It is based on trees. In each bifurcation of the tree, the algorithm identifies the best marker (discrete or continuous) and the optimal drugs for the patients in each branch of the tree. Since the algorithm is very fast, it can be transformed into a random optimizing forest or an extreme gradient boost method. Another advantage is the simplicity of the method: the trees are self-explanatory and easy to understand.



Applied contribution with an impact in the social sphere, innovation or knowledge transfer


Contribution: An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation.

Author(s): Guillermo M. Besné, Alejandro Horrillo-Maysonnial, María Jesús Nicolás, Ferran Capell-Pascual, Elena Urrestarazu, Julio Artieda, Miguel Valencia

Brief description of the qualities motivating the award: This work uses canned Matlab ML functions to implement 6 different ML methods for the detection of events from EEG data. The authors analyze the signals both in the time and frequency domain. It is an interdisciplinary work, which gives it an added value. They build customized ML models for the detection of ictal and interictal activities for the automatic annotation of epileptic traits based on electrophysiological recordings. It appears to require considerable input from the user, although the claim is made that the interactive app is simpler than available methods.


Methodological contribution


Contribution: BOSO: A novel feature selection algorithm for linear regression with high-dimensional data.

Author(s): Luis V. Valcárcel, Edurne San José-Enériz, Xabier Cendoya, Ángel Rubio, Xabier Agirre, Felipe Prósper, Francisco J. Planes

Brief description of the qualities motivating the award: The paper presents a new method for feature selection in high dimensional regression, and empirically demonstrates based on synthetic datasets how it works better than Lasso, forward selection, best subsets, and the relaxed lasso methods. It performs very well for data that can be modeled by a linear regression model. The broad applicability makes this an appealing work.